Visual Search Image Processing for Watermark Extraction
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Solution Overview
Problem
Existing image processing technologies for visual search on portable devices face challenges in reliability and efficiency, particularly in extracting information from low-quality images and varying imaging conditions, leading to increased battery consumption and failure under certain conditions.
Innovation Solution
The approach involves capturing a burst of image frames with varying camera settings and resolutions, applying a common data-extraction process to each frame, and using subtraction-based filtering to enhance the signal-to-noise ratio of watermark signals, thereby simplifying image processing and reducing computational intensity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple decoding attempts are applied to a single image frame, then reliability of data extraction is improved, but processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary actions by capturing multiple image frames with varying camera settings before the data extraction process. This allows the extraction process to work with pre-prepared variations of the image, reducing the need for multiple decoding attempts on the same frame and thereby decreasing processing time while maintaining reliability.
Solution Approach 2:
The imaging process is segmented into multiple captures with different camera settings (focus distance, aperture, resolution) rather than processing a single image through multiple decoding attempts. This segmentation approach distributes the computational workload across parallel processing of different image frames, reducing total processing time.
2Reliability
If multiple alternative detectors are applied to an image frame, then reliability of data extraction is improved, but device complexity increases
Solution Approach 1:
Instead of using multiple alternative detectors on a single image, the system performs preliminary capture of multiple images with varying settings. A single data extraction process is then applied to each captured image, simplifying the detector architecture while maintaining reliability through the variety of input images.
Solution Approach 2:
A single data extraction process is designed to be universal and can process images captured with different camera settings. This multi-functional approach eliminates the need for multiple specialized detectors, reducing device complexity while maintaining the ability to extract data reliably under varying conditions.
3Reliability
If image enhancement is applied to low quality portions, then data extraction reliability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary capture of multiple images with varying focus distances and resolutions, ensuring that at least one image is already in optimal quality for data extraction. This eliminates the need for time-consuming enhancement or repair processes on low-quality portions, as the pre-captured varied images provide ready-to-process high-quality data.
4Measurement precision
If higher resolution imaging is used, then data extraction accuracy is improved, but battery power consumption increases
Solution Approach 1:
The imaging process is segmented into capturing multiple frames at different resolutions and focus distances, then processing only the necessary frames for data extraction. This segmentation allows the system to achieve high accuracy when needed while avoiding continuous high-resolution capture, thereby reducing overall battery power consumption.
Solution Approach 2:
The system dynamically changes camera parameters (resolution, focus distance, aperture) based on the specific extraction task requirements. Rather than consistently using high resolution, the system adjusts parameters to balance accuracy needs with power consumption, capturing multiple parameter sets and selecting the most appropriate for processing.
Data Source
AI summary
Certain aspects of the present technology involve automated capture of several image frames (e.g., simultaneously in a single exposure, or in a burst of exposures), and application of a data-extraction process (e.g., watermark decoding) to each such image. Other aspects of the technology involve capturing a single scene at two different resolutions, and submitting imagery at both resolutions for watermark decoding. Still other aspects of the technology involve increasing the signal-to-noise ratio of a watermark signal by subtracting one image from another. Yet other aspects of the technology involve receiving focus distance data from a camera, and employing such data in extracting information from camera imagery. Smartphone camera APIs can be employed to simplify implementation of such methods. A great number of features and arrangements are also detailed. Embodiments of such technology can simplify image processing required for data extraction, with attendant reductions in required program memory and battery power consumption. Moreover, they can enlarge a system's operational envelope—enabling information to be extracted from imagery under conditions that lead to failure in prior art arrangements.